Receptiveness of and Implementation Considerations for COVID-19 Vaccination Certificates in Asia: A Survey Across 9 Countries
Bibliographic record
Abstract
COVID-19 vaccination certificates (CVCs) have played a key role in safe reopening of borders for international travel and trade, so understanding key stakeholder perceptions of enablers and barriers for their effective use is critical. The COVID-19 Vaccination Policy Research and Deci-sion-Support Initiative in Asia (CORESIA) was established to address policy questions related to CVCs. We conducted two online surveys, i.e., one for the public and one for health and non-health sector experts, from June to October 2021 in nine Asian countries. Descriptive analysis identified participants, enablers, and barriers. Most participants (78% public, 89% experts) accepted the use of CVCs, primarily to resume international travel (76%). Most respondents in both surveys wanted the minimum vaccination coverage to be 60% before CVCs were implemented nation-wide. Most of the public (82%) agreed to maintain existing non-pharmaceutical interventions, while most experts wanted risk-based testing and quarantine policy for incoming travellers (51%) and both digital and paper format CVCs (64%). Support for CVCs for international travel remains high in Asia. Recognising key enablers and barriers for effective use of CVCs from COVID-19 pandemic may help policymakers draft effective border policies for future epidemics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".